A New Way of Accounting for Ecological UncertaintyJuly 29, 2008Oceans1 Comment

Managing populations can be tricky when you don’t know very much about them. But now it’s easier to decide what to do, thanks to a new way of accounting for ecological uncertainty. Even better, an analysis of this method shows that a relatively simple approach is often the best.

This gives hope for “robust conservation strategies that have relatively low data demands,” say John Harwood of the University of St. Andrews, U.K., and Kevin Stokes of the New Zealand Seafood Industry Council in the December issue of Trends in Ecology and Evolution.

Not accounting for uncertainty properly can be disastrous, as has been famously exemplified by the widespread failure of fisheries managers to harvest stocks sustainably. But fisheries management is now enjoying a turnaround. “Some of the most interesting methods for taking account of uncertainty in ecological systems have been developed by fisheries scientists,” say Harwood and Stokes.

The beauty of the new decision-making approach is that it lets biologists give rigorous but accessible advice on the risks of various management options. Rooted in operational research — which provides a quantitative basis for decision making — the approach involves developing a computer model with three components:

(1) a biological model that accounts for processes such as population growth and migration;

(2) an observation model that accounts for ways of gathering information about these biological processes, which can have built-in errors; and

(3) a management model that accounts for how fisheries might affect biological processes.

Because biological processes for many species are little known, the best management strategies are those that are low-risk over a range of biologically plausible scenarios. For example, the Scientific Committee of the International Whaling Commission successfully used the new approach to revise the management of baleen whales, even though little is known about the spatial structure of the populations or the potential effects of climate change.

Harwood and Stokes report that this new decision-making approach can yield management options that are both sound and relatively simple. For the eastern stock of the Australian gemfish (Rexea solandri), for example, a model based on less detailed catch information was better than one based on more detailed information. “The simpler procedure resulted in less variable catches and could be implemented with data that were relatively cheap to collect,” they note.

Although the results of this new approach are accessible to decision makers, Harwood and Stokes acknowledge that the math behind these models can intimidate many ecologists. Thus, they call for user-friendly software to help ecologists develop and apply these models.